scholarly journals Transmission Line Obstacle Detection Based on Structural Constraint and Feature Fusion

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 452
Author(s):  
Xuhui Ye ◽  
Dong Wang ◽  
Daode Zhang ◽  
Xinyu Hu
Keyword(s):  
Feature Fusion ◽  
Recognition Rate ◽  
Obstacle Detection ◽  
Support Vector ◽  
Structural Constraints ◽  
Structural Constraint ◽  
Image Block ◽  
Long Distance ◽  
Data Set ◽  
Multi Scale

Accurate detection and identification of obstacles plays an important role in the navigation and behavior planning of the patrol robot. Aiming at the patrol robot with camera mounted symmetrically, an obstacle detection method based on structural constraint and feature fusion is proposed. Firstly, in order to discover the region of interest, the bounding box algorithm is used to propose the region. The location of the detected ground wire is used to constrain the region, and the image block of interest is clipped. Secondly, in order to accurately represent the multi-view and multi-scale obstacle images, the global shape features and the improved local corner features are fused by different weights. Then, the particle swarm-optimized support vector machine (PSO-SVM) is used for classifying and recognizing obstacles. On block data set B containing multi-view and multi-scale obstacle images, the recognition rate of this method can reach up to 86.2%, which shows the effectiveness of weighted fusion of global and local features. On data set A containing complete images of different distances, the detection success rate of long-distance obstacles can reach 80.2%. The validity of the proposed method based on structural constraints and feature fusion is verified.

scholarly journals A Fast and Lightweight Method with Feature Fusion and Multi-Context for Face Detection

2018 ◽  
Vol 10 (8) ◽  
pp. 80
Author(s):  
Lei Zhang ◽  
Xiaoli Zhi
Keyword(s):  
Face Detection ◽  
Graphics Processing Units ◽  
High Performance ◽  
Feature Fusion ◽  
Local Context ◽  
Data Set ◽  
Global Context ◽  
Detection Algorithms ◽  
Multi Scale ◽  
Benchmark Datasets

Convolutional neural networks (CNN for short) have made great progress in face detection. They mostly take computation intensive networks as the backbone in order to obtain high precision, and they cannot get a good detection speed without the support of high-performance GPUs (Graphics Processing Units). This limits CNN-based face detection algorithms in real applications, especially in some speed dependent ones. To alleviate this problem, we propose a lightweight face detector in this paper, which takes a fast residual network as backbone. Our method can run fast even on cheap and ordinary GPUs. To guarantee its detection precision, multi-scale features and multi-context are fully exploited in efficient ways. Specifically, feature fusion is used to obtain semantic strongly multi-scale features firstly. Then multi-context including both local and global context is added to these multi-scale features without extra computational burden. The local context is added through a depthwise separable convolution based approach, and the global context by a simple global average pooling way. Experimental results show that our method can run at about 110 fps on VGA (Video Graphics Array)-resolution images, while still maintaining competitive precision on WIDER FACE and FDDB (Face Detection Data Set and Benchmark) datasets as compared with its state-of-the-art counterparts.

scholarly journals Local Non Zero Eigen Value Preservation Based Expression Recognition

2020 ◽  
Vol 8 (6) ◽  
pp. 3823-3832
Keyword(s):  
Discriminant Analysis ◽  
Feature Vector ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Fusion Rule ◽  
Classification Performance ◽  
Support Vector ◽  
Expression Recognition ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant

This work proposes an finest mapping from features space to inherited space using kernel locality non zero eigen values protecting Fisher discriminant analysis subspace approach. This approach is designed by cascading analytical and non-inherited face texture features. Both Gabor magnitude feature vector (GMFV) and phase feature vector (GPFV) are independently accessed. Feature fusion is carried out by cascading geometrical distance feature vector (GDFV) with Gabor magnitude and phase vectors. Feature fusion dataset space is converted into short dimensional inherited space by kernel locality protecting Fisher discriminant analysis method and projected space is normalized by suitable normalization technique to prevent dissimilarity between scores. Final scores of projected domains are fused using greatest fusion rule. Expressions are classified using Euclidean distance matching and support vector machine radial basis function kernel classifier. An experimental outcome emphasizes that the proposed approach is efficient for dimension reduction, competent recognition and classification. Performance of proposed approach is deliberated in comparison with connected subspace approaches. The finest average recognition rate achieves 97.61% for JAFFE and 81.48% YALE database respectively.

Automated Text Detection and Recognition in Annotated Biomedical Publication Images

2014 ◽  
Vol 9 (2) ◽  
pp. 34-63 ◽  
Author(s):  
Soumya De ◽  
R. Joe Stanley ◽  
Beibei Cheng ◽  
Sameer Antani ◽  
Rodney Long ◽  
...  
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Recognition Rate ◽  
Regions Of Interest ◽  
Correct Recognition ◽  
Support Vector ◽  
Data Set ◽  
Biomedical Publication ◽  
Biomedical Publications ◽  
Detection And Recognition

Images in biomedical publications often convey important information related to an article's content. When referenced properly, these images aid in clinical decision support. Annotations such as text labels and symbols, as provided by medical experts, are used to highlight regions of interest within the images. These annotations, if extracted automatically, could be used in conjunction with either the image caption text or the image citations (mentions) in the articles to improve biomedical information retrieval. In the current study, automatic detection and recognition of text labels in biomedical publication images was investigated. This paper presents both image analysis and feature-based approaches to extract and recognize specific regions of interest (text labels) within images in biomedical publications. Experiments were performed on 6515 characters extracted from text labels present in 200 biomedical publication images. These images are part of the data set from ImageCLEF 2010. Automated character recognition experiments were conducted using geometry-, region-, exemplar-, and profile-based correlation features and Fourier descriptors extracted from the characters. Correct recognition as high as 92.67% was obtained with a support vector machine classifier, compared to a 75.90% correct recognition rate with a benchmark Optical Character Recognition technique.

scholarly journals Adaptive Multi-Scale Feature Fusion Based Residual U-net for Fracture Segmentation in Coal Rock Images

2020 ◽  
Author(s):  
Fengli Lu ◽  
Chengcai Fu ◽  
Guoying Zhang ◽  
Jie Shi
Keyword(s):  
Spatial Information ◽  
Feature Fusion ◽  
Ct Images ◽  
Published Data ◽  
Rock Fractures ◽  
Feature Maps ◽  
Data Set ◽  
Scale Feature ◽  
Multi Scale ◽  
Coal Rock

Abstract Accurate segmentation of fractures in coal rock CT images is important for safe production and the development of coalbed methane. However, the coal rock fractures formed through natural geological evolution, which are complex, low contrast and different scales. Furthermore, there is no published data set of coal rock. In this paper, we proposed adaptive multi-scale feature fusion based residual U-uet (AMSFFR-U-uet) for fracture segmentation in coal rock CT images. The dilated residual blocks (DResBlock) with dilated ratio (1,2,3) are embedded into encoding branch of the U-uet structure, which can improve the ability of extract feature of network and capture different scales fractures. Furthermore, feature maps of different sizes in the encoding branch are concatenated by adaptive multi-scale feature fusion (AMSFF) module. And AMSFF can not only capture different scales fractures but also improve the restoration of spatial information. To alleviate the lack of coal rock fractures training data, we applied a set of comprehensive data augmentation operations to increase the diversity of training samples. Our network, U-net and Res-U-net are tested on our test set of coal rock CT images with five different region coal rock samples. The experimental results show that our proposed approach improve the average Dice coefficient by 2.9%, the average precision by 7.2% and the average Recall by 9.1% , respectively. Therefore, AMSFFR-U-net can achieve better segmentation results of coal rock fractures, and has stronger generalization ability and robustness.

Mine Loadometer License Plate Recognition Based on MFF-DS

2013 ◽  
Vol 712-715 ◽  
pp. 2341-2344 ◽  
Author(s):  
Xiu Cai Guo ◽  
Shi Qian Zhang
Keyword(s):  
Feature Fusion ◽  
Recognition Rate ◽  
Evidence Theory ◽  
Support Vector ◽  
License Plate ◽  
Final Decision ◽  
Fusion Method ◽  
License Plate Recognition ◽  
Single Feature ◽  
Basic Probability

The result of license plate recognition with a single feature is unsatisfactory. A multi-feature fusion method based on D-S evidence theory is proposed to improve results of mine loadometer license plate recognition. Firstly, three kinds of features including contour, projection and trellis-coded are extracted from the vehicle plate character image. Then the Basic Probability Assignment (BPA) is defined to get the credibility of recognition results by using the multi-class Support Vector Machine (SVM) with one-against-one method. Finally, D-S evidence theory is employed to integrate the credibility of evidences for making a final decision. The experimental results show that the multi-feature fusion method has higher recognition rate, fault tolerance and robustness.

scholarly journals Partial discharge feature extraction based on synchrosqueezed windowed Fourier transform and multi-scale dispersion entropy

2020 ◽  
Vol 53 (7-8) ◽  
pp. 1078-1087
Author(s):  
Wang Wenbo ◽  
Sun Lin ◽  
Wang Bin ◽  
Yu Min
Keyword(s):  
Feature Extraction ◽  
Fourier Transform ◽  
Partial Discharge ◽  
Recognition Rate ◽  
Entropy Method ◽  
Support Vector ◽  
Discharge Mode ◽  
Windowed Fourier Transform ◽  
Multi Scale ◽  
Original Feature

The recognition of partial discharge mode is an important indicator of the insulation condition in transformers, based on which maintenance can be arranged. Discharge feature extraction is the key to recognize discharge mode. To solve the problem of poor stability and low recognition rate of partial discharge mode, this paper proposes a feature extraction method based on synchrosqueezed windowed Fourier transform and multi-scale dispersion entropy. First, the four partial discharge signals collected under laboratory conditions are decomposed by synchrosqueezed windowed Fourier transform, then a number of band-limited intrinsic mode type functions are obtained, and the original feature quantities of partial discharge signals are obtained by calculating the multi-scale dispersion entropies of each intrinsic mode type function. Based on that, original feature quantity is optimized by using the maximum relevance and minimum redundancy criteria. Finally, the classification is implemented by the support vector machine. Experimental results show that in the case of noise interference, the proposed synchrosqueezed windowed Fourier transform–multi-scale dispersion entropy method can still accurately describe the feature of different discharge signals and has a higher recognition rate than both the empirical mode decomposition–multi-scale dispersion entropy method and the direct multi-scale dispersion entropy method.

scholarly journals Evaluation of Image Forgery Detection Using Multi-Scale Weber Local Descriptors

2015 ◽  
Vol 24 (04) ◽  
pp. 1540016 ◽  
Author(s):  
Muhammad Hussain ◽  
Sahar Qasem ◽  
George Bebis ◽  
Ghulam Muhammad ◽  
Hatim Aboalsamh ◽  
...  
Keyword(s):  
Digital Image ◽  
Image Data ◽  
Feature Space ◽  
Detection Methods ◽  
Support Vector ◽  
Data Sets ◽  
Forgery Detection ◽  
Data Set ◽  
Multi Scale ◽  
Copy Move Forgery Detection

Due to the maturing of digital image processing techniques, there are many tools that can forge an image easily without leaving visible traces and lead to the problem of the authentication of digital images. Based on the assumption that forgery alters the texture micro-patterns in a digital image and texture descriptors can be used for modeling this change; we employed two stat-of-the-art local texture descriptors: multi-scale Weber's law descriptor (multi-WLD) and multi-scale local binary pattern (multi-LBP) for splicing and copy-move forgery detection. As the tamper traces are not visible to open eyes, so the chrominance components of an image encode these traces and were used for modeling tamper traces with the texture descriptors. To reduce the dimension of the feature space and get rid of redundant features, we employed locally learning based (LLB) algorithm. For identifying an image as authentic or tampered, Support vector machine (SVM) was used. This paper presents the thorough investigation for the validation of this forgery detection method. The experiments were conducted on three benchmark image data sets, namely, CASIA v1.0, CASIA v2.0, and Columbia color. The experimental results showed that the accuracy rate of multi-WLD based method was 94.19% on CASIA v1.0, 96.52% on CASIA v2.0, and 94.17% on Columbia data set. It is not only significantly better than multi-LBP based method, but also it outperforms other stat-of-the-art similar forgery detection methods.

scholarly journals Recognition of Fetal Facial Ultrasound Standard Plane Based on Texture Feature Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiaoli Wang ◽  
Zhonghua Liu ◽  
Yongzhao Du ◽  
Yong Diao ◽  
Peizhong Liu ◽  
...  
Keyword(s):  
Cleft Lip ◽  
Cleft Lip And Palate ◽  
Feature Fusion ◽  
Texture Feature ◽  
Recall Rate ◽  
Support Vector ◽  
Ultrasound Images ◽  
Fusion Method ◽  
Accurate Identification ◽  
Data Set

In the process of prenatal ultrasound diagnosis, accurate identification of fetal facial ultrasound standard plane (FFUSP) is essential for accurate facial deformity detection and disease screening, such as cleft lip and palate detection and Down syndrome screening check. However, the traditional method of obtaining standard planes is manual screening by doctors. Due to different levels of doctors, this method often leads to large errors in the results. Therefore, in this study, we propose a texture feature fusion method (LH-SVM) for automatic recognition and classification of FFUSP. First, extract image’s texture features, including Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG), then perform feature fusion, and finally adopt Support Vector Machine (SVM) for predictive classification. In our study, we used fetal facial ultrasound images from 20 to 24 weeks of gestation as experimental data for a total of 943 standard plane images (221 ocular axial planes, 298 median sagittal planes, 424 nasolabial coronal planes, and 350 nonstandard planes, OAP, MSP, NCP, N-SP). Based on this data set, we performed five-fold cross-validation. The final test results show that the accuracy rate of the proposed method for FFUSP classification is 94.67%, the average precision rate is 94.27%, the average recall rate is 93.88%, and the average F 1 score is 94.08%. The experimental results indicate that the texture feature fusion method can effectively predict and classify FFUSP, which provides an essential basis for clinical research on the automatic detection method of FFUSP.

Research On Exercise Fatigue Estimation Method of Pilates Rehabilitation Based On ECG and sEMG Feature Fusion

2021 ◽  
Author(s):  
Dujuan Li ◽  
Caixia Chen
Keyword(s):  
Feature Fusion ◽  
Recognition Rate ◽  
Estimation Method ◽  
Classification Model ◽  
Support Vector ◽  
Ecg Signal ◽  
Data Types ◽  
Semg Signal ◽  
Linear Discriminant ◽  
Fatigue Estimation

Abstract Purpose. Fatigue estimation is of great significance to improve the accuracy of intention recognition and avoid secondary injury in Pilates rehabilitation. Surface electromyography (sEMG) is used to estimate fatigue with low and unstable recognition rates. To improve the rate, this paper fused electrocardiogram (ECG) signal and sEMG signal under three different states, and the classification model of the improved proved particle swarm optimization support vector machine (IPSO-SVM) algorithm was established. Methods. Twenty subjects performed 150 minutes of Pilates rehabilitation exercise. ECG and sEMG signals were collected at the same time. After necessary preprocessing, the IPSO-SVM classification model based on feature fusion was established to identify three different fatigue states (relaxed, transition, and tired). The model effects of different classification algorithms and different fused data types were compared. Results. Compared with common physiological signal classification methods such as BP neural network algorithm(BPNN), K-nearest neighbor(KNN), and Linear discriminant analysis(LDA), IPSO-SVM had obvious advantages in the classification effect of sEMG and ECG signals, the average recognition rate was 87.83%. The recognition rates of sEMG and ECG fusion feature classification models were 94.25%, 92.25%, 94.25%. The recognition accuracy and model performance was significantly improved. Conclusion. The sEMG and ECG signal after feature fusion form a complementary mechanism. At the same time, IPOS-SVM can accurately detect the fatigue state in the process of Pilates rehabilitation. This study establishes technical support for establishing relevant man-machine devices and improving the safety of Pilates rehabilitation.

scholarly journals Wearable Internet-of-Things platform for human activity recognition and health care

2020 ◽  
Vol 16 (6) ◽  
pp. 155014772091156 ◽  
Author(s):  
Asif Iqbal ◽  
Farman Ullah ◽  
Hafeez Anwar ◽  
Ata Ur Rehman ◽  
Kiran Shah ◽  
...  
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Internet Of Things ◽  
Activity Recognition ◽  
Wearable Sensors ◽  
Recognition Rate ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Data Set

We propose to perform wearable sensors-based human physical activity recognition. This is further extended to an Internet-of-Things (IoT) platform which is based on a web-based application that integrates wearable sensors, smartphones, and activity recognition. To this end, a smartphone collects the data from wearable sensors and sends it to the server for processing and recognition of the physical activity. We collect a novel data set of 13 physical activities performed both indoor and outdoor. The participants are from both the genders where their number per activity varies. During these activities, the wearable sensors measure various body parameters via accelerometers, gyroscope, magnetometers, pressure, and temperature. These measurements and their statistical are then represented in features vectors that used to train and test supervised machine learning algorithms (classifiers) for activity recognition. On the given data set, we evaluate a number of widely known classifiers such random forests, support vector machine, and many others using the WEKA machine learning suite. Using the default settings of these classifiers in WEKA, we attain the highest overall classification accuracy of 90%. Consequently, such a recognition rate is encouraging, reliable, and effective to be used in the proposed platform.

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